Overview

Dataset statistics

Number of variables21
Number of observations207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.1 KiB
Average record size in memory168.6 B

Variable types

Categorical3
DateTime2
Numeric16

Alerts

TEAM_ID has constant value ""Constant
ACC_P is highly overall correlated with ACC_R and 9 other fieldsHigh correlation
ACC_R is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
ACC_Y is highly overall correlated with ACC_P and 10 other fieldsHigh correlation
ALTITUDE is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
FLIGHT_SOFTWARE_STATE is highly overall correlated with ACC_P and 11 other fieldsHigh correlation
GNSS_ALTITUDE is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
GNSS_LATITUDE is highly overall correlated with ACC_P and 11 other fieldsHigh correlation
GNSS_LONGITUDE is highly overall correlated with GNSS_LATITUDEHigh correlation
GNSS_SATS is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
GYRO_Y is highly overall correlated with FLIGHT_SOFTWARE_STATEHigh correlation
PACKET_COUNT is highly overall correlated with ACC_Y and 2 other fieldsHigh correlation
PRESSURE is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
TEMP is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
VOLTAGE is highly overall correlated with ACC_P and 9 other fieldsHigh correlation
eCO2 is highly imbalanced (76.0%)Imbalance
GNSS_TIME has unique valuesUnique
PACKET_COUNT has 10 (4.8%) zerosZeros
ALTITUDE has 32 (15.5%) zerosZeros
ACC_R has 3 (1.4%) zerosZeros
ACC_P has 5 (2.4%) zerosZeros
ACC_Y has 4 (1.9%) zerosZeros
GYRO_R has 39 (18.8%) zerosZeros
GYRO_P has 53 (25.6%) zerosZeros
GYRO_Y has 28 (13.5%) zerosZeros
TVOC has 54 (26.1%) zerosZeros

Reproduction

Analysis started2024-07-13 07:16:15.702423
Analysis finished2024-07-13 07:17:29.570753
Duration1 minute and 13.87 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

TEAM_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<2022ASI-049
207 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2484
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<2022ASI-049
2nd row<2022ASI-049
3rd row<2022ASI-049
4th row<2022ASI-049
5th row<2022ASI-049

Common Values

ValueCountFrequency (%)
<2022ASI-049 207
100.0%

Length

2024-07-13T07:17:29.854062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-13T07:17:30.224998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2022asi-049 207
100.0%

Most occurring characters

ValueCountFrequency (%)
2 621
25.0%
0 414
16.7%
< 207
 
8.3%
A 207
 
8.3%
S 207
 
8.3%
I 207
 
8.3%
- 207
 
8.3%
4 207
 
8.3%
9 207
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1449
58.3%
Uppercase Letter 621
25.0%
Math Symbol 207
 
8.3%
Dash Punctuation 207
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 621
42.9%
0 414
28.6%
4 207
 
14.3%
9 207
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
A 207
33.3%
S 207
33.3%
I 207
33.3%
Math Symbol
ValueCountFrequency (%)
< 207
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1863
75.0%
Latin 621
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 621
33.3%
0 414
22.2%
< 207
 
11.1%
- 207
 
11.1%
4 207
 
11.1%
9 207
 
11.1%
Latin
ValueCountFrequency (%)
A 207
33.3%
S 207
33.3%
I 207
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 621
25.0%
0 414
16.7%
< 207
 
8.3%
A 207
 
8.3%
S 207
 
8.3%
I 207
 
8.3%
- 207
 
8.3%
4 207
 
8.3%
9 207
 
8.3%
Distinct121
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2024-07-13 00:00:11
Maximum2024-07-13 00:02:23
2024-07-13T07:17:30.631025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:31.163217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PACKET_COUNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.768116
Minimum-1
Maximum74
Zeros10
Zeros (%)4.8%
Negative10
Negative (%)4.8%
Memory size1.7 KiB
2024-07-13T07:17:31.654380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q14
median12
Q324
95-th percentile63.7
Maximum74
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.827272
Coefficient of variation (CV)1.0564338
Kurtosis0.63172341
Mean18.768116
Median Absolute Deviation (MAD)9
Skewness1.2850015
Sum3885
Variance393.12073
MonotonicityNot monotonic
2024-07-13T07:17:32.186617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 10
 
4.8%
0 10
 
4.8%
1 9
 
4.3%
3 9
 
4.3%
4 9
 
4.3%
2 9
 
4.3%
5 8
 
3.9%
6 7
 
3.4%
7 6
 
2.9%
8 6
 
2.9%
Other values (66) 124
59.9%
ValueCountFrequency (%)
-1 10
4.8%
0 10
4.8%
1 9
4.3%
2 9
4.3%
3 9
4.3%
4 9
4.3%
5 8
3.9%
6 7
3.4%
7 6
2.9%
8 6
2.9%
ValueCountFrequency (%)
74 1
0.5%
73 1
0.5%
72 1
0.5%
71 1
0.5%
70 1
0.5%
69 1
0.5%
68 1
0.5%
67 1
0.5%
66 1
0.5%
65 1
0.5%

ALTITUDE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct136
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean463.35362
Minimum0
Maximum840.4
Zeros32
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:32.647771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.25
median591.9
Q3837.9
95-th percentile838.87
Maximum840.4
Range840.4
Interquartile range (IQR)825.65

Descriptive statistics

Standard deviation380.27639
Coefficient of variation (CV)0.82070446
Kurtosis-1.8277276
Mean463.35362
Median Absolute Deviation (MAD)246.6
Skewness-0.27947041
Sum95914.2
Variance144610.13
MonotonicityNot monotonic
2024-07-13T07:17:33.115081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
15.5%
838.2 8
 
3.9%
838.5 7
 
3.4%
838.4 6
 
2.9%
838.1 5
 
2.4%
838.3 5
 
2.4%
837.9 3
 
1.4%
837.7 3
 
1.4%
838.6 3
 
1.4%
838 3
 
1.4%
Other values (126) 132
63.8%
ValueCountFrequency (%)
0 32
15.5%
0.2 2
 
1.0%
0.3 3
 
1.4%
0.4 2
 
1.0%
0.6 1
 
0.5%
0.8 1
 
0.5%
1 1
 
0.5%
1.1 1
 
0.5%
1.2 1
 
0.5%
1.4 1
 
0.5%
ValueCountFrequency (%)
840.4 1
0.5%
840.2 1
0.5%
840 1
0.5%
839.9 1
0.5%
839.7 1
0.5%
839.6 1
0.5%
839.3 2
1.0%
839.2 1
0.5%
839 1
0.5%
838.9 1
0.5%

PRESSURE
Real number (ℝ)

HIGH CORRELATION 

Distinct145
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95408.159
Minimum91629
Maximum100283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:33.607775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum91629
5-th percentile91646.3
Q191657
median94414
Q3100096
95-th percentile100255
Maximum100283
Range8654
Interquartile range (IQR)8439

Descriptive statistics

Standard deviation3781.995
Coefficient of variation (CV)0.039640164
Kurtosis-1.7414707
Mean95408.159
Median Absolute Deviation (MAD)2763
Skewness0.31147445
Sum19749489
Variance14303486
MonotonicityNot monotonic
2024-07-13T07:17:33.978389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100255 10
 
4.8%
91653 8
 
3.9%
100254 7
 
3.4%
91651 6
 
2.9%
91652 5
 
2.4%
91655 5
 
2.4%
91650 5
 
2.4%
100257 4
 
1.9%
100253 4
 
1.9%
91657 3
 
1.4%
Other values (135) 150
72.5%
ValueCountFrequency (%)
91629 1
0.5%
91632 1
0.5%
91633 1
0.5%
91635 1
0.5%
91637 1
0.5%
91639 1
0.5%
91641 2
1.0%
91642 1
0.5%
91645 1
0.5%
91646 1
0.5%
ValueCountFrequency (%)
100283 1
 
0.5%
100258 1
 
0.5%
100257 4
 
1.9%
100256 3
 
1.4%
100255 10
4.8%
100254 7
3.4%
100253 4
 
1.9%
100252 2
 
1.0%
100251 2
 
1.0%
100250 1
 
0.5%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.7343
Minimum62
Maximum67.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:34.221807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile62.1
Q162.1
median64.9
Q366.8
95-th percentile67.6
Maximum67.7
Range5.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.2085738
Coefficient of variation (CV)0.034117521
Kurtosis-1.6709503
Mean64.7343
Median Absolute Deviation (MAD)2.4
Skewness-0.1091762
Sum13400
Variance4.8777984
MonotonicityNot monotonic
2024-07-13T07:17:34.471343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
62.1 48
23.2%
62.2 17
 
8.2%
66.8 17
 
8.2%
67.3 11
 
5.3%
62 10
 
4.8%
67.6 10
 
4.8%
64.8 10
 
4.8%
67.5 9
 
4.3%
66 7
 
3.4%
64.7 7
 
3.4%
Other values (19) 61
29.5%
ValueCountFrequency (%)
62 10
 
4.8%
62.1 48
23.2%
62.2 17
 
8.2%
62.3 1
 
0.5%
63.5 2
 
1.0%
63.6 6
 
2.9%
64.7 7
 
3.4%
64.8 10
 
4.8%
64.9 5
 
2.4%
65.5 1
 
0.5%
ValueCountFrequency (%)
67.7 7
3.4%
67.6 10
4.8%
67.5 9
4.3%
67.4 1
 
0.5%
67.3 11
5.3%
67.2 1
 
0.5%
67 3
 
1.4%
66.9 4
 
1.9%
66.8 17
8.2%
66.7 1
 
0.5%

VOLTAGE
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8548309
Minimum6.39
Maximum7.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:34.749185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6.39
5-th percentile6.46
Q16.79
median6.94
Q36.99
95-th percentile7.03
Maximum7.03
Range0.64
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.18280406
Coefficient of variation (CV)0.026667917
Kurtosis0.012698836
Mean6.8548309
Median Absolute Deviation (MAD)0.07
Skewness-1.1392151
Sum1418.95
Variance0.033417326
MonotonicityNot monotonic
2024-07-13T07:17:35.051893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.03 24
 
11.6%
6.96 16
 
7.7%
6.95 13
 
6.3%
7.01 10
 
4.8%
6.94 10
 
4.8%
7 9
 
4.3%
6.91 8
 
3.9%
6.99 7
 
3.4%
6.97 7
 
3.4%
6.54 6
 
2.9%
Other values (42) 97
46.9%
ValueCountFrequency (%)
6.39 1
 
0.5%
6.41 3
1.4%
6.42 2
1.0%
6.44 1
 
0.5%
6.45 2
1.0%
6.46 3
1.4%
6.49 1
 
0.5%
6.5 3
1.4%
6.51 1
 
0.5%
6.52 1
 
0.5%
ValueCountFrequency (%)
7.03 24
11.6%
7.02 4
 
1.9%
7.01 10
4.8%
7 9
 
4.3%
6.99 7
 
3.4%
6.98 6
 
2.9%
6.97 7
 
3.4%
6.96 16
7.7%
6.95 13
6.3%
6.94 10
4.8%

GNSS_TIME
Date

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2024-07-13 16:25:56
Maximum2024-07-13 16:35:03
2024-07-13T07:17:35.334198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:35.615730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

GNSS_LATITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.114613
Minimum23.1143
Maximum23.1151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:36.587210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum23.1143
5-th percentile23.1143
Q123.1143
median23.1146
Q323.1148
95-th percentile23.115
Maximum23.1151
Range0.0008
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00027916991
Coefficient of variation (CV)1.2077637 × 10-5
Kurtosis-1.3974193
Mean23.114613
Median Absolute Deviation (MAD)0.0003
Skewness0.27267171
Sum4784.7249
Variance7.7935838 × 10-8
MonotonicityNot monotonic
2024-07-13T07:17:36.850019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
23.1143 66
31.9%
23.115 37
17.9%
23.1148 30
14.5%
23.1146 25
 
12.1%
23.1145 21
 
10.1%
23.1144 10
 
4.8%
23.1151 8
 
3.9%
23.1147 6
 
2.9%
23.1149 4
 
1.9%
ValueCountFrequency (%)
23.1143 66
31.9%
23.1144 10
 
4.8%
23.1145 21
 
10.1%
23.1146 25
 
12.1%
23.1147 6
 
2.9%
23.1148 30
14.5%
23.1149 4
 
1.9%
23.115 37
17.9%
23.1151 8
 
3.9%
ValueCountFrequency (%)
23.1151 8
 
3.9%
23.115 37
17.9%
23.1149 4
 
1.9%
23.1148 30
14.5%
23.1147 6
 
2.9%
23.1146 25
 
12.1%
23.1145 21
 
10.1%
23.1144 10
 
4.8%
23.1143 66
31.9%

GNSS_LONGITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.498512
Minimum72.4981
Maximum72.499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:37.200660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum72.4981
5-th percentile72.4981
Q172.4983
median72.4986
Q372.4987
95-th percentile72.499
Maximum72.499
Range0.0009
Interquartile range (IQR)0.0004

Descriptive statistics

Standard deviation0.00029106578
Coefficient of variation (CV)4.0147829 × 10-6
Kurtosis-1.2164508
Mean72.498512
Median Absolute Deviation (MAD)0.0002
Skewness-0.043206088
Sum15007.192
Variance8.4719291 × 10-8
MonotonicityNot monotonic
2024-07-13T07:17:37.635218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
72.4987 69
33.3%
72.4981 42
20.3%
72.4983 28
13.5%
72.4984 24
 
11.6%
72.499 19
 
9.2%
72.4988 9
 
4.3%
72.4986 8
 
3.9%
72.4989 4
 
1.9%
72.4982 4
 
1.9%
ValueCountFrequency (%)
72.4981 42
20.3%
72.4982 4
 
1.9%
72.4983 28
13.5%
72.4984 24
 
11.6%
72.4986 8
 
3.9%
72.4987 69
33.3%
72.4988 9
 
4.3%
72.4989 4
 
1.9%
72.499 19
 
9.2%
ValueCountFrequency (%)
72.499 19
 
9.2%
72.4989 4
 
1.9%
72.4988 9
 
4.3%
72.4987 69
33.3%
72.4986 8
 
3.9%
72.4984 24
 
11.6%
72.4983 28
13.5%
72.4982 4
 
1.9%
72.4981 42
20.3%

GNSS_ALTITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439.10193
Minimum0
Maximum775.2
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:38.093867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.6
Q157.5
median551.1
Q3744.9
95-th percentile773.1
Maximum775.2
Range775.2
Interquartile range (IQR)687.4

Descriptive statistics

Standard deviation314.1449
Coefficient of variation (CV)0.7154259
Kurtosis-1.7898412
Mean439.10193
Median Absolute Deviation (MAD)222
Skewness-0.27823124
Sum90894.1
Variance98687.016
MonotonicityNot monotonic
2024-07-13T07:17:38.639314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
773.1 29
 
14.0%
656.3 19
 
9.2%
55.8 10
 
4.8%
55.9 8
 
3.9%
56.3 7
 
3.4%
744.9 6
 
2.9%
745.4 6
 
2.9%
56.1 6
 
2.9%
717 6
 
2.9%
55.1 4
 
1.9%
Other values (99) 106
51.2%
ValueCountFrequency (%)
0 1
 
0.5%
54.9 1
 
0.5%
55 2
 
1.0%
55.1 4
 
1.9%
55.4 1
 
0.5%
55.5 1
 
0.5%
55.6 2
 
1.0%
55.7 1
 
0.5%
55.8 10
4.8%
55.9 8
3.9%
ValueCountFrequency (%)
775.2 1
 
0.5%
773.5 1
 
0.5%
773.1 29
14.0%
772.6 1
 
0.5%
771.9 1
 
0.5%
771.7 1
 
0.5%
771.2 1
 
0.5%
770.2 1
 
0.5%
768.6 1
 
0.5%
766.7 1
 
0.5%

GNSS_SATS
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.364058
Minimum18
Maximum357.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:39.070812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q121
median24
Q327
95-th percentile28
Maximum357.36
Range339.36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.468501
Coefficient of variation (CV)0.92526603
Kurtosis197.11536
Mean25.364058
Median Absolute Deviation (MAD)3
Skewness13.870511
Sum5250.36
Variance550.77055
MonotonicityNot monotonic
2024-07-13T07:17:39.482732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
27 53
25.6%
21 36
17.4%
28 34
16.4%
18 29
14.0%
24 16
 
7.7%
22 10
 
4.8%
19 8
 
3.9%
23 8
 
3.9%
26 7
 
3.4%
20 3
 
1.4%
Other values (2) 3
 
1.4%
ValueCountFrequency (%)
18 29
14.0%
19 8
 
3.9%
20 3
 
1.4%
21 36
17.4%
22 10
 
4.8%
23 8
 
3.9%
24 16
 
7.7%
25 2
 
1.0%
26 7
 
3.4%
27 53
25.6%
ValueCountFrequency (%)
357.36 1
 
0.5%
28 34
16.4%
27 53
25.6%
26 7
 
3.4%
25 2
 
1.0%
24 16
 
7.7%
23 8
 
3.9%
22 10
 
4.8%
21 36
17.4%
20 3
 
1.4%

ACC_R
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7784058
Minimum-3.44
Maximum19.48
Zeros3
Zeros (%)1.4%
Negative44
Negative (%)21.3%
Memory size1.7 KiB
2024-07-13T07:17:39.937757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-3.44
5-th percentile-0.879
Q10.065
median15.37
Q316.725
95-th percentile18.407
Maximum19.48
Range22.92
Interquartile range (IQR)16.66

Descriptive statistics

Standard deviation8.2206456
Coefficient of variation (CV)0.84069384
Kurtosis-1.8040591
Mean9.7784058
Median Absolute Deviation (MAD)2.4
Skewness-0.36020703
Sum2024.13
Variance67.579013
MonotonicityNot monotonic
2024-07-13T07:17:40.454272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.17 3
 
1.4%
16.34 3
 
1.4%
-0.07 3
 
1.4%
-0.11 3
 
1.4%
0 3
 
1.4%
-0.12 3
 
1.4%
15.67 2
 
1.0%
15.84 2
 
1.0%
14.85 2
 
1.0%
17.57 2
 
1.0%
Other values (165) 181
87.4%
ValueCountFrequency (%)
-3.44 1
0.5%
-3.12 1
0.5%
-2.66 1
0.5%
-2.39 1
0.5%
-1.55 1
0.5%
-1.21 1
0.5%
-1.12 1
0.5%
-0.99 1
0.5%
-0.97 2
1.0%
-0.9 1
0.5%
ValueCountFrequency (%)
19.48 1
0.5%
19.13 1
0.5%
19.04 1
0.5%
18.91 1
0.5%
18.87 2
1.0%
18.59 1
0.5%
18.58 1
0.5%
18.45 1
0.5%
18.44 2
1.0%
18.33 1
0.5%

ACC_P
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct171
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.605604
Minimum-23.92
Maximum4.49
Zeros5
Zeros (%)2.4%
Negative161
Negative (%)77.8%
Memory size1.7 KiB
2024-07-13T07:17:40.916538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-23.92
5-th percentile-21.89
Q1-20.23
median-18.38
Q3-0.05
95-th percentile1.79
Maximum4.49
Range28.41
Interquartile range (IQR)20.18

Descriptive statistics

Standard deviation9.96271
Coefficient of variation (CV)-0.8584396
Kurtosis-1.7986207
Mean-11.605604
Median Absolute Deviation (MAD)3.21
Skewness0.3449001
Sum-2402.36
Variance99.25559
MonotonicityNot monotonic
2024-07-13T07:17:41.435228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 5
 
2.4%
-21.61 3
 
1.4%
0.25 3
 
1.4%
-0.26 3
 
1.4%
0.02 3
 
1.4%
-22.04 2
 
1.0%
-19.01 2
 
1.0%
-20.26 2
 
1.0%
-0.07 2
 
1.0%
-19.27 2
 
1.0%
Other values (161) 180
87.0%
ValueCountFrequency (%)
-23.92 1
0.5%
-23.57 1
0.5%
-23.53 1
0.5%
-23.09 1
0.5%
-22.31 1
0.5%
-22.28 1
0.5%
-22.27 1
0.5%
-22.24 1
0.5%
-22.04 2
1.0%
-21.98 1
0.5%
ValueCountFrequency (%)
4.49 1
0.5%
4.17 1
0.5%
3.22 1
0.5%
3.12 1
0.5%
3.01 1
0.5%
2.37 1
0.5%
2.28 1
0.5%
2.13 1
0.5%
2.01 1
0.5%
1.84 1
0.5%

ACC_Y
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct179
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.89343
Minimum-14.14
Maximum2.76
Zeros4
Zeros (%)1.9%
Negative165
Negative (%)79.7%
Memory size1.7 KiB
2024-07-13T07:17:41.914780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-14.14
5-th percentile-12.112
Q1-10.195
median-8.4
Q3-0.06
95-th percentile1.174
Maximum2.76
Range16.9
Interquartile range (IQR)10.135

Descriptive statistics

Standard deviation5.0825146
Coefficient of variation (CV)-0.8624035
Kurtosis-1.6751186
Mean-5.89343
Median Absolute Deviation (MAD)3.2
Skewness0.27705901
Sum-1219.94
Variance25.831955
MonotonicityNot monotonic
2024-07-13T07:17:42.430412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 4
 
1.9%
0.05 4
 
1.9%
-0.05 4
 
1.9%
-8.53 3
 
1.4%
-0.06 3
 
1.4%
0.08 2
 
1.0%
-10.56 2
 
1.0%
-10.54 2
 
1.0%
-8.03 2
 
1.0%
-9.52 2
 
1.0%
Other values (169) 179
86.5%
ValueCountFrequency (%)
-14.14 1
0.5%
-12.91 1
0.5%
-12.69 1
0.5%
-12.59 1
0.5%
-12.58 1
0.5%
-12.46 1
0.5%
-12.39 1
0.5%
-12.38 1
0.5%
-12.33 1
0.5%
-12.26 1
0.5%
ValueCountFrequency (%)
2.76 1
0.5%
2.33 1
0.5%
2.22 1
0.5%
1.91 1
0.5%
1.67 1
0.5%
1.59 1
0.5%
1.47 1
0.5%
1.4 1
0.5%
1.2 1
0.5%
1.19 1
0.5%

GYRO_R
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.035362319
Minimum-3.49
Maximum0.41
Zeros39
Zeros (%)18.8%
Negative113
Negative (%)54.6%
Memory size1.7 KiB
2024-07-13T07:17:42.891155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-3.49
5-th percentile-0.117
Q1-0.06
median-0.02
Q30.01
95-th percentile0.07
Maximum0.41
Range3.9
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.25111932
Coefficient of variation (CV)-7.101325
Kurtosis176.04361
Mean-0.035362319
Median Absolute Deviation (MAD)0.04
Skewness-12.713514
Sum-7.32
Variance0.063060912
MonotonicityNot monotonic
2024-07-13T07:17:43.395689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 39
18.8%
-0.04 16
 
7.7%
-0.02 13
 
6.3%
-0.06 12
 
5.8%
-0.08 10
 
4.8%
0.02 10
 
4.8%
-0.09 9
 
4.3%
-0.07 9
 
4.3%
-0.05 9
 
4.3%
0.01 9
 
4.3%
Other values (24) 71
34.3%
ValueCountFrequency (%)
-3.49 1
 
0.5%
-0.21 1
 
0.5%
-0.2 1
 
0.5%
-0.17 1
 
0.5%
-0.15 1
 
0.5%
-0.14 3
1.4%
-0.13 2
 
1.0%
-0.12 1
 
0.5%
-0.11 5
2.4%
-0.1 3
1.4%
ValueCountFrequency (%)
0.41 1
 
0.5%
0.31 1
 
0.5%
0.17 1
 
0.5%
0.15 1
 
0.5%
0.14 1
 
0.5%
0.1 2
 
1.0%
0.09 3
1.4%
0.07 5
2.4%
0.06 2
 
1.0%
0.05 7
3.4%

GYRO_P
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.018067633
Minimum-4.37
Maximum0.13
Zeros53
Zeros (%)25.6%
Negative63
Negative (%)30.4%
Memory size1.7 KiB
2024-07-13T07:17:43.877631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-4.37
5-th percentile-0.07
Q1-0.02
median0
Q30.03
95-th percentile0.09
Maximum0.13
Range4.5
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.31443915
Coefficient of variation (CV)-17.40345
Kurtosis180.69737
Mean-0.018067633
Median Absolute Deviation (MAD)0.03
Skewness-13.109694
Sum-3.74
Variance0.098871976
MonotonicityNot monotonic
2024-07-13T07:17:44.343307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 53
25.6%
0.03 20
 
9.7%
0.02 12
 
5.8%
0.05 11
 
5.3%
0.04 10
 
4.8%
-0.01 10
 
4.8%
-0.03 10
 
4.8%
-0.05 9
 
4.3%
-0.04 9
 
4.3%
0.06 9
 
4.3%
Other values (18) 54
26.1%
ValueCountFrequency (%)
-4.37 1
 
0.5%
-0.93 1
 
0.5%
-0.14 2
 
1.0%
-0.12 3
 
1.4%
-0.11 1
 
0.5%
-0.1 1
 
0.5%
-0.09 1
 
0.5%
-0.07 4
1.9%
-0.06 3
 
1.4%
-0.05 9
4.3%
ValueCountFrequency (%)
0.13 1
 
0.5%
0.12 1
 
0.5%
0.11 2
 
1.0%
0.1 3
 
1.4%
0.09 6
2.9%
0.08 5
2.4%
0.07 3
 
1.4%
0.06 9
4.3%
0.05 11
5.3%
0.04 10
4.8%

GYRO_Y
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038695652
Minimum-0.82
Maximum0.51
Zeros28
Zeros (%)13.5%
Negative50
Negative (%)24.2%
Memory size1.7 KiB
2024-07-13T07:17:44.817684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.82
5-th percentile-0.1
Q10
median0.03
Q30.095
95-th percentile0.187
Maximum0.51
Range1.33
Interquartile range (IQR)0.095

Descriptive statistics

Standard deviation0.10861323
Coefficient of variation (CV)2.8068588
Kurtosis20.114909
Mean0.038695652
Median Absolute Deviation (MAD)0.05
Skewness-2.1614457
Sum8.01
Variance0.011796834
MonotonicityNot monotonic
2024-07-13T07:17:45.346248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 28
 
13.5%
0.09 15
 
7.2%
0.03 14
 
6.8%
0.06 11
 
5.3%
0.07 10
 
4.8%
0.13 9
 
4.3%
0.01 8
 
3.9%
0.11 8
 
3.9%
-0.02 8
 
3.9%
-0.03 8
 
3.9%
Other values (32) 88
42.5%
ValueCountFrequency (%)
-0.82 1
 
0.5%
-0.32 1
 
0.5%
-0.25 1
 
0.5%
-0.22 1
 
0.5%
-0.14 1
 
0.5%
-0.12 2
1.0%
-0.11 2
1.0%
-0.1 3
1.4%
-0.09 1
 
0.5%
-0.08 2
1.0%
ValueCountFrequency (%)
0.51 1
 
0.5%
0.32 1
 
0.5%
0.24 1
 
0.5%
0.22 2
1.0%
0.21 1
 
0.5%
0.2 3
1.4%
0.19 2
1.0%
0.18 1
 
0.5%
0.17 3
1.4%
0.15 4
1.9%

FLIGHT_SOFTWARE_STATE
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
127 
2
61 
3
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters207
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

Length

2024-07-13T07:17:45.833947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-13T07:17:46.218209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
61.4%
2 61
29.5%
3 19
 
9.2%

TVOC
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.594203
Minimum0
Maximum1566
Zeros54
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-07-13T07:17:46.610013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q33
95-th percentile170
Maximum1566
Range1566
Interquartile range (IQR)3

Descriptive statistics

Standard deviation295.99214
Coefficient of variation (CV)3.9155402
Kurtosis20.510388
Mean75.594203
Median Absolute Deviation (MAD)2
Skewness4.6772297
Sum15648
Variance87611.349
MonotonicityNot monotonic
2024-07-13T07:17:47.083716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 76
36.7%
0 54
26.1%
1 26
 
12.6%
1529 7
 
3.4%
5 7
 
3.4%
170 6
 
2.9%
32 5
 
2.4%
33 4
 
1.9%
34 4
 
1.9%
169 4
 
1.9%
Other values (10) 14
 
6.8%
ValueCountFrequency (%)
0 54
26.1%
1 26
 
12.6%
3 76
36.7%
5 7
 
3.4%
6 1
 
0.5%
8 1
 
0.5%
32 5
 
2.4%
33 4
 
1.9%
34 4
 
1.9%
35 2
 
1.0%
ValueCountFrequency (%)
1566 1
 
0.5%
1529 7
3.4%
170 6
2.9%
169 4
1.9%
164 3
1.4%
83 1
 
0.5%
78 2
 
1.0%
77 1
 
0.5%
38 1
 
0.5%
36 1
 
0.5%

eCO2
Categorical

IMBALANCE 

Distinct14
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
400>
183 
401>
 
6
404>
 
3
406>
 
2
412>
 
2
Other values (9)
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters828
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.4%

Sample

1st row400>
2nd row400>
3rd row400>
4th row400>
5th row400>

Common Values

ValueCountFrequency (%)
400> 183
88.4%
401> 6
 
2.9%
404> 3
 
1.4%
406> 2
 
1.0%
412> 2
 
1.0%
414> 2
 
1.0%
425> 2
 
1.0%
402> 1
 
0.5%
415> 1
 
0.5%
430> 1
 
0.5%
Other values (4) 4
 
1.9%

Length

2024-07-13T07:17:47.354749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
400 183
88.4%
401 6
 
2.9%
404 3
 
1.4%
406 2
 
1.0%
412 2
 
1.0%
414 2
 
1.0%
425 2
 
1.0%
402 1
 
0.5%
415 1
 
0.5%
430 1
 
0.5%
Other values (4) 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 381
46.0%
4 212
25.6%
> 207
25.0%
1 11
 
1.3%
2 6
 
0.7%
5 5
 
0.6%
3 3
 
0.4%
6 2
 
0.2%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 621
75.0%
Math Symbol 207
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 381
61.4%
4 212
34.1%
1 11
 
1.8%
2 6
 
1.0%
5 5
 
0.8%
3 3
 
0.5%
6 2
 
0.3%
9 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
> 207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 381
46.0%
4 212
25.6%
> 207
25.0%
1 11
 
1.3%
2 6
 
0.7%
5 5
 
0.6%
3 3
 
0.4%
6 2
 
0.2%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 381
46.0%
4 212
25.6%
> 207
25.0%
1 11
 
1.3%
2 6
 
0.7%
5 5
 
0.6%
3 3
 
0.4%
6 2
 
0.2%
9 1
 
0.1%

Interactions

2024-07-13T07:17:25.401172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:24.466859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:29.452210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:34.166600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-07-13T07:17:16.365202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:20.899461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:24.607598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:28.048780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:28.565606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:33.281349image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:38.103259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:41.751179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:44.808844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:48.316266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:51.421689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:56.762124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:01.523508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:05.414433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:09.496730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:12.783725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:16.582304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:21.210786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:24.822982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:28.237612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:28.867470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:33.553619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:38.389000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:41.923775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:44.984281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:48.510807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:51.593556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:57.074665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:01.819753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:05.679929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:09.787657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:13.555733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:16.784952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:21.490898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:25.006649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:28.440953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:29.182481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:33.855271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:38.687600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:42.115479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:45.174032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:48.727585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:52.291769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:16:57.313197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:02.125945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:05.861287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:10.091795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:13.746913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:16.988461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:21.815650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-13T07:17:25.198338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-07-13T07:17:47.559882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ACC_PACC_RACC_YALTITUDEFLIGHT_SOFTWARE_STATEGNSS_ALTITUDEGNSS_LATITUDEGNSS_LONGITUDEGNSS_SATSGYRO_PGYRO_RGYRO_YPACKET_COUNTPRESSURETEMPTVOCVOLTAGEeCO2
ACC_P1.000-0.6720.681-0.7480.704-0.749-0.6780.446-0.746-0.0090.287-0.4280.4330.747-0.7500.3000.5870.000
ACC_R-0.6721.000-0.6840.7130.6750.6980.676-0.4340.7150.203-0.4620.377-0.441-0.7160.719-0.252-0.5930.000
ACC_Y0.681-0.6841.000-0.7060.696-0.700-0.6700.419-0.7020.0010.284-0.4120.5070.712-0.7100.2310.5920.000
ALTITUDE-0.7480.713-0.7061.0000.6680.9470.821-0.4620.9030.178-0.3090.418-0.383-0.9950.936-0.273-0.7470.225
FLIGHT_SOFTWARE_STATE0.7040.6750.6960.6681.000-0.771-0.7560.481-0.795-0.1470.377-0.5240.6310.776-0.7860.2730.6190.000
GNSS_ALTITUDE-0.7490.698-0.7000.947-0.7711.0000.845-0.4950.9010.166-0.2910.438-0.413-0.9470.976-0.301-0.7450.247
GNSS_LATITUDE-0.6780.676-0.6700.821-0.7560.8451.000-0.7100.8020.128-0.3090.388-0.520-0.8400.855-0.244-0.6680.132
GNSS_LONGITUDE0.446-0.4340.419-0.4620.481-0.495-0.7101.000-0.3910.0080.142-0.2550.2390.448-0.4870.4220.3300.095
GNSS_SATS-0.7460.715-0.7020.903-0.7950.9010.802-0.3911.0000.131-0.3210.448-0.352-0.9090.938-0.210-0.7660.000
GYRO_P-0.0090.2030.0010.178-0.1470.1660.1280.0080.1311.000-0.0790.088-0.108-0.1790.1560.043-0.1010.000
GYRO_R0.287-0.4620.284-0.3090.377-0.291-0.3090.142-0.321-0.0791.000-0.2010.2490.313-0.302-0.0360.2980.000
GYRO_Y-0.4280.377-0.4120.418-0.5240.4380.388-0.2550.4480.088-0.2011.000-0.267-0.4220.436-0.086-0.3860.000
PACKET_COUNT0.433-0.4410.507-0.3830.631-0.413-0.5200.239-0.352-0.1080.249-0.2671.0000.424-0.3900.2940.3080.000
PRESSURE0.747-0.7160.712-0.9950.776-0.947-0.8400.448-0.909-0.1790.313-0.4220.4241.000-0.9380.2500.7530.193
TEMP-0.7500.719-0.7100.936-0.7860.9760.855-0.4870.9380.156-0.3020.436-0.390-0.9381.000-0.284-0.7380.246
TVOC0.300-0.2520.231-0.2730.273-0.301-0.2440.422-0.2100.043-0.036-0.0860.2940.250-0.2841.0000.2100.000
VOLTAGE0.587-0.5930.592-0.7470.619-0.745-0.6680.330-0.766-0.1010.298-0.3860.3080.753-0.7380.2101.0000.089
eCO20.0000.0000.0000.2250.0000.2470.1320.0950.0000.0000.0000.0000.0000.1930.2460.0000.0891.000

Missing values

2024-07-13T07:17:28.793568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-13T07:17:29.348723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TEAM_IDTIME_STAMPINGPACKET_COUNTALTITUDEPRESSURETEMPVOLTAGEGNSS_TIMEGNSS_LATITUDEGNSS_LONGITUDEGNSS_ALTITUDEGNSS_SATSACC_RACC_PACC_YGYRO_RGYRO_PGYRO_YFLIGHT_SOFTWARE_STATETVOCeCO2
0<2022ASI-0490:01:09-10.010025562.17.0016:25:5623.114372.498756.318.0-0.190.320.080.000.000.0000400>
1<2022ASI-0490:01:0900.310024962.17.0316:25:5723.114372.498756.318.0-0.39-0.19-0.130.020.000.0603400>
2<2022ASI-0490:01:1011.010024162.07.0116:25:5823.114372.498756.218.0-0.12-0.270.41-0.020.000.0523400>
3<2022ASI-0490:01:1120.010025462.17.0116:25:5923.114372.498756.318.00.090.260.10-0.04-0.000.0323400>
4<2022ASI-0490:01:1230.010025362.17.0316:26:0023.114372.498756.318.00.360.25-0.23-0.020.03-0.0723400>
5<2022ASI-0490:01:1340.010025762.17.0316:26:0123.114372.498756.318.0-0.340.25-0.760.040.02-0.0423400>
6<2022ASI-0490:01:1450.010025262.17.0316:26:0223.114372.498756.318.0-0.300.08-0.00-0.060.00-0.3223400>
7<2022ASI-0490:01:1560.010025562.17.0316:26:0323.114372.498756.318.0-0.30-0.17-0.29-0.060.00-0.2523400>
8<2022ASI-0490:01:1670.010025562.17.0316:26:0423.114372.498756.218.00.360.64-0.270.05-0.01-0.1223400>
9<2022ASI-0490:01:1780.010025562.17.0316:26:0523.114372.498756.218.0-0.180.750.050.05-0.000.0623400>
TEAM_IDTIME_STAMPINGPACKET_COUNTALTITUDEPRESSURETEMPVOLTAGEGNSS_TIMEGNSS_LATITUDEGNSS_LONGITUDEGNSS_ALTITUDEGNSS_SATSACC_RACC_PACC_YGYRO_RGYRO_PGYRO_YFLIGHT_SOFTWARE_STATETVOCeCO2
197<2022ASI-0490:00:4716838.39165267.66.4116:34:5423.11572.4981773.128.016.10-20.24-10.540.01-0.120.0401405>
198<2022ASI-0490:00:4817838.59165167.66.9116:34:5523.11572.4981773.128.017.52-18.93-10.32-0.010.060.1101400>
199<2022ASI-0490:00:4918838.29165467.66.8316:34:5623.11572.4981773.128.015.69-19.65-8.96-0.020.050.2001400>
200<2022ASI-0490:00:5019838.29165367.76.9116:34:5723.11572.4981773.128.017.09-21.11-9.030.05-0.02-0.0301400>
201<2022ASI-0490:00:5120838.59165067.76.5416:34:5823.11572.4981773.128.014.84-21.98-12.390.170.040.0301400>
202<2022ASI-0490:00:5221838.49165267.76.8316:34:5923.11572.4981773.128.016.17-20.26-10.56-0.04-0.030.0000400>
203<2022ASI-0490:00:5322838.39165367.76.9416:35:0023.11572.4981773.128.017.23-18.90-11.040.020.080.0301400>
204<2022ASI-0490:00:5423838.39165267.76.7516:35:0123.11572.4981773.128.017.24-18.61-9.61-0.030.010.0601400>
205<2022ASI-0490:00:5524838.19165567.76.9016:35:0223.11572.4981773.128.016.97-11.35-10.480.41-0.930.0701400>
206<2022ASI-0490:00:5625838.99164667.76.8316:35:0323.11572.4981773.128.013.45-6.97-14.14-3.49-4.37-0.8201400>